{"id":"W4229030211","doi":"10.1093/annweh/wxab083","title":"The TRACTOR Project: TRACking and MoniToring Occupational Risks in Agriculture Using French Insurance Health Data (MSA)","year":2021,"lang":"en","type":"article","venue":"Annals of Work Exposures and Health","topic":"Agriculture and Farm Safety","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal","funders":"Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail; Agence Nationale de la Recherche","keywords":"Tractor; Workforce; Agriculture; Occupational safety and health; Business; Work (physics); Environmental health; Disease surveillance; Database; Medicine; Public health; Computer science; Engineering; Geography; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007180705,0.0008517965,0.0005028404,0.006038463,0.0005640733,0.001691899,0.00131917,0.0006935922,0.00436959],"category_scores_gemma":[0.01622249,0.000271903,0.001157434,0.004245363,0.0002204775,0.0007581511,0.002149202,0.0005137792,0.001333049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002205372,"about_ca_system_score_gemma":0.005567042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.164779,"about_ca_topic_score_gemma":0.08883472,"domain_scores_codex":[0.9952023,0.002421146,0.0005105786,0.0007775743,0.0007909123,0.0002974079],"domain_scores_gemma":[0.988628,0.002992191,0.002322958,0.001572247,0.003576953,0.0009075746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007994331,0.0003394117,0.7203687,0.001469865,0.001328353,0.0004509305,0.001552875,0.005394828,0.002171665,0.002797876,0.1180145,0.1453117],"study_design_scores_gemma":[0.0003069238,0.0005774276,0.8121293,0.001195903,0.0004575622,0.0004044113,0.00227867,0.01756373,0.001991326,0.001267383,0.1617205,0.0001067965],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3754528,0.003186325,0.02535834,0.003605138,0.0001685304,0.002410685,0.5807981,0.002160347,0.00685974],"genre_scores_gemma":[0.4004784,0.001626399,0.06918745,0.0007161507,0.0002352259,0.003221458,0.5199927,0.0002859925,0.004256254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.164779,"threshold_uncertainty_score":0.3276398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3896190325273604,"score_gpt":0.4249105234778277,"score_spread":0.03529149095046724,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}